Abstract: The paper presents new results concerning selection of
optimal information fusion formula for ensembles of C-OTDR
channels. The goal of information fusion is to create an integral
classificator designed for effective classification of seismoacoustic
target events. The LPBoost (LP-β and LP-B variants), the Multiple
Kernel Learning, and Weighing of Inversely as Lipschitz Constants
(WILC) approaches were compared. The WILC is a brand new
approach to optimal fusion of Lipschitz Classifiers Ensembles.
Results of practical usage are presented.
Abstract: An optimal mean-square fusion formulas with scalar
and matrix weights are presented. The relationship between them is
established. The fusion formulas are compared on the continuous-time
filtering problem. The basic differential equation for cross-covariance
of the local errors being the key quantity for distributed fusion is
derived. It is shown that the fusion filters are effective for multi-sensor
systems containing different types of sensors. An example
demonstrating the reasonable good accuracy of the proposed filters is
given.
Abstract: This paper reports on a receding horizon filtering for
mobile robot systems with cross-correlated sensor noises and
uncertainties. Also, the effect of uncertain parameters in the state of
the tracking error model performance is considered. A distributed
fusion receding horizon filter is proposed. The distributed fusion
filtering algorithm represents the optimal linear combination of the
local filters under the minimum mean square error criterion. The
derivation of the error cross-covariances between the local receding
horizon filters is the key of this paper. Simulation results of the
tracking mobile robot-s motion demonstrate high accuracy and
computational efficiency of the distributed fusion receding horizon
filter.